Overview

Dataset statistics

Number of variables14
Number of observations331234
Missing cells53924
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.4 MiB
Average record size in memory112.0 B

Variable types

Categorical1
DateTime3
Numeric9
Text1

Alerts

VERSIE has constant value ""Constant
DATUM_BESTAND has constant value ""Constant
PEILDATUM has constant value ""Constant
BEHANDELEND_SPECIALISME_CD is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_PAT_PER_ZPD is highly overall correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly overall correlated with AANTAL_PAT_PER_ZPDHigh correlation
AANTAL_PAT_PER_DIAG is highly overall correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly overall correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly overall correlated with BEHANDELEND_SPECIALISME_CD and 1 other fieldsHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
GEMIDDELDE_VERKOOPPRIJS has 53924 (16.3%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.16585803)Skewed

Reproduction

Analysis started2023-06-21 12:44:33.733351
Analysis finished2023-06-21 12:44:54.389734
Duration20.66 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
1.0
331234 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters993702
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 331234
100.0%

Length

2023-06-21T12:44:54.457962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-21T12:44:54.599411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 331234
100.0%

Most occurring characters

ValueCountFrequency (%)
1 331234
33.3%
. 331234
33.3%
0 331234
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 662468
66.7%
Other Punctuation 331234
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 331234
50.0%
0 331234
50.0%
Other Punctuation
ValueCountFrequency (%)
. 331234
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 993702
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 331234
33.3%
. 331234
33.3%
0 331234
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 993702
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 331234
33.3%
. 331234
33.3%
0 331234
33.3%

DATUM_BESTAND
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Minimum2023-06-07 00:00:00
Maximum2023-06-07 00:00:00
2023-06-21T12:44:54.695550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:54.805455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

PEILDATUM
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Minimum2023-06-01 00:00:00
Maximum2023-06-01 00:00:00
2023-06-21T12:44:54.910953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:55.018905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

JAAR
Date

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Minimum2012-01-01 00:00:00
Maximum2023-01-01 00:00:00
2023-06-21T12:44:55.129352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:55.249149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

BEHANDELEND_SPECIALISME_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean445.2422
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-06-21T12:44:55.395354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile361
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation1016.7909
Coefficient of variation (CV)2.2836805
Kurtosis57.389814
Mean445.2422
Median Absolute Deviation (MAD)8
Skewness7.7012982
Sum1.4747935 × 108
Variance1033863.8
MonotonicityNot monotonic
2023-06-21T12:44:55.549845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 46612
14.1%
313 42974
13.0%
303 38212
11.5%
330 26164
 
7.9%
316 22451
 
6.8%
308 17728
 
5.4%
306 13887
 
4.2%
324 13522
 
4.1%
301 13285
 
4.0%
304 10778
 
3.3%
Other values (18) 85621
25.8%
ValueCountFrequency (%)
301 13285
 
4.0%
302 7296
 
2.2%
303 38212
11.5%
304 10778
 
3.3%
305 46612
14.1%
306 13887
 
4.2%
307 5839
 
1.8%
308 17728
 
5.4%
310 3663
 
1.1%
313 42974
13.0%
ValueCountFrequency (%)
8418 4562
 
1.4%
8416 730
 
0.2%
1900 222
 
0.1%
390 922
 
0.3%
389 3502
 
1.1%
362 4357
 
1.3%
361 2410
 
0.7%
335 3340
 
1.0%
330 26164
7.9%
329 874
 
0.3%
Distinct1900
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2023-06-21T12:44:56.028934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3512894
Min length2

Characters and Unicode

Total characters1110061
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st row09
2nd row16
3rd row06
4th row08
5th row17
ValueCountFrequency (%)
101 1424
 
0.4%
402 1374
 
0.4%
403 1333
 
0.4%
301 1332
 
0.4%
201 1261
 
0.4%
203 1247
 
0.4%
401 1119
 
0.3%
404 1104
 
0.3%
409 1083
 
0.3%
302 1071
 
0.3%
Other values (1890) 318886
96.3%
2023-06-21T12:44:56.689769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 212402
19.1%
0 203795
18.4%
2 147046
13.2%
3 120342
10.8%
5 85530
7.7%
9 79983
 
7.2%
4 78714
 
7.1%
7 65332
 
5.9%
6 57889
 
5.2%
8 47808
 
4.3%
Other values (15) 11220
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1098841
99.0%
Uppercase Letter 11220
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2127
19.0%
M 1882
16.8%
B 1346
12.0%
Z 949
8.5%
E 938
8.4%
D 741
 
6.6%
A 723
 
6.4%
F 709
 
6.3%
C 365
 
3.3%
K 363
 
3.2%
Other values (5) 1077
9.6%
Decimal Number
ValueCountFrequency (%)
1 212402
19.3%
0 203795
18.5%
2 147046
13.4%
3 120342
11.0%
5 85530
7.8%
9 79983
 
7.3%
4 78714
 
7.2%
7 65332
 
5.9%
6 57889
 
5.3%
8 47808
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1098841
99.0%
Latin 11220
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 2127
19.0%
M 1882
16.8%
B 1346
12.0%
Z 949
8.5%
E 938
8.4%
D 741
 
6.6%
A 723
 
6.4%
F 709
 
6.3%
C 365
 
3.3%
K 363
 
3.2%
Other values (5) 1077
9.6%
Common
ValueCountFrequency (%)
1 212402
19.3%
0 203795
18.5%
2 147046
13.4%
3 120342
11.0%
5 85530
7.8%
9 79983
 
7.3%
4 78714
 
7.2%
7 65332
 
5.9%
6 57889
 
5.3%
8 47808
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1110061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 212402
19.1%
0 203795
18.4%
2 147046
13.2%
3 120342
10.8%
5 85530
7.7%
9 79983
 
7.2%
4 78714
 
7.1%
7 65332
 
5.9%
6 57889
 
5.2%
8 47808
 
4.3%
Other values (15) 11220
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6094
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4043384 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-06-21T12:44:57.058665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999038
Q199799061
median1.4959903 × 108
Q39.9000302 × 108
95-th percentile9.9051605 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9020396 × 108

Descriptive statistics

Standard deviation4.2886734 × 108
Coefficient of variation (CV)0.97373839
Kurtosis-1.7354955
Mean4.4043384 × 108
Median Absolute Deviation (MAD)1.1960002 × 108
Skewness0.46948435
Sum1.4588666 × 1014
Variance1.8392719 × 1017
MonotonicityNot monotonic
2023-06-21T12:44:57.240969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2423
 
0.7%
990004007 2366
 
0.7%
990003004 2295
 
0.7%
990004006 1900
 
0.6%
990356076 1722
 
0.5%
131999228 1609
 
0.5%
990356073 1603
 
0.5%
131999164 1572
 
0.5%
990003007 1481
 
0.4%
131999194 1403
 
0.4%
Other values (6084) 312860
94.5%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 11
< 0.1%
10501004 12
< 0.1%
10501005 12
< 0.1%
10501007 3
 
< 0.1%
10501008 12
< 0.1%
10501010 12
< 0.1%
10501011 3
 
< 0.1%
11101002 10
< 0.1%
11101003 12
< 0.1%
ValueCountFrequency (%)
998418081 165
< 0.1%
998418080 148
< 0.1%
998418079 38
 
< 0.1%
998418077 9
 
< 0.1%
998418076 9
 
< 0.1%
998418075 7
 
< 0.1%
998418074 228
0.1%
998418073 222
0.1%
998418072 9
 
< 0.1%
998418071 9
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION 

Distinct10286
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean518.98908
Minimum1
Maximum165128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-06-21T12:44:57.418960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median13
Q3101
95-th percentile1756
Maximum165128
Range165127
Interquartile range (IQR)98

Descriptive statistics

Standard deviation3218.4739
Coefficient of variation (CV)6.2014289
Kurtosis401.77703
Mean518.98908
Median Absolute Deviation (MAD)12
Skewness16.617483
Sum1.7190683 × 108
Variance10358574
MonotonicityNot monotonic
2023-06-21T12:44:57.590023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 55658
 
16.8%
2 27102
 
8.2%
3 17591
 
5.3%
4 12817
 
3.9%
5 10013
 
3.0%
6 8510
 
2.6%
7 7066
 
2.1%
8 5948
 
1.8%
9 5423
 
1.6%
10 4817
 
1.5%
Other values (10276) 176289
53.2%
ValueCountFrequency (%)
1 55658
16.8%
2 27102
8.2%
3 17591
 
5.3%
4 12817
 
3.9%
5 10013
 
3.0%
6 8510
 
2.6%
7 7066
 
2.1%
8 5948
 
1.8%
9 5423
 
1.6%
10 4817
 
1.5%
ValueCountFrequency (%)
165128 1
< 0.1%
162289 1
< 0.1%
155871 1
< 0.1%
154259 1
< 0.1%
154257 1
< 0.1%
145500 1
< 0.1%
144716 1
< 0.1%
118397 1
< 0.1%
115938 1
< 0.1%
113248 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct11051
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean614.9957
Minimum1
Maximum240002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-06-21T12:44:57.771595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3111
95-th percentile2002
Maximum240002
Range240001
Interquartile range (IQR)108

Descriptive statistics

Standard deviation4148.3667
Coefficient of variation (CV)6.7453589
Kurtosis710.04832
Mean614.9957
Median Absolute Deviation (MAD)13
Skewness21.165858
Sum2.0370748 × 108
Variance17208946
MonotonicityNot monotonic
2023-06-21T12:44:57.951766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 53606
 
16.2%
2 26630
 
8.0%
3 17426
 
5.3%
4 12623
 
3.8%
5 9925
 
3.0%
6 8483
 
2.6%
7 6998
 
2.1%
8 5891
 
1.8%
9 5343
 
1.6%
10 4840
 
1.5%
Other values (11041) 179469
54.2%
ValueCountFrequency (%)
1 53606
16.2%
2 26630
8.0%
3 17426
 
5.3%
4 12623
 
3.8%
5 9925
 
3.0%
6 8483
 
2.6%
7 6998
 
2.1%
8 5891
 
1.8%
9 5343
 
1.6%
10 4840
 
1.5%
ValueCountFrequency (%)
240002 1
< 0.1%
232423 1
< 0.1%
231954 1
< 0.1%
230966 1
< 0.1%
227936 1
< 0.1%
227409 1
< 0.1%
226320 1
< 0.1%
223891 1
< 0.1%
218673 1
< 0.1%
215053 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct9153
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7786.4381
Minimum1
Maximum230257
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-06-21T12:44:58.126503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33
Q1380
median1706
Q36398
95-th percentile37219.05
Maximum230257
Range230256
Interquartile range (IQR)6018

Descriptive statistics

Standard deviation18115.485
Coefficient of variation (CV)2.3265433
Kurtosis33.791106
Mean7786.4381
Median Absolute Deviation (MAD)1571
Skewness5.0385032
Sum2.579133 × 109
Variance3.281708 × 108
MonotonicityNot monotonic
2023-06-21T12:44:58.300531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 655
 
0.2%
3 642
 
0.2%
1 621
 
0.2%
2 620
 
0.2%
4 596
 
0.2%
17 594
 
0.2%
21 593
 
0.2%
12 575
 
0.2%
8 567
 
0.2%
19 566
 
0.2%
Other values (9143) 325205
98.2%
ValueCountFrequency (%)
1 621
0.2%
2 620
0.2%
3 642
0.2%
4 596
0.2%
5 520
0.2%
6 546
0.2%
7 545
0.2%
8 567
0.2%
9 655
0.2%
10 492
0.1%
ValueCountFrequency (%)
230257 23
< 0.1%
227944 23
< 0.1%
217928 24
< 0.1%
217721 19
< 0.1%
214510 17
< 0.1%
213517 25
< 0.1%
211593 17
< 0.1%
210418 19
< 0.1%
205347 17
< 0.1%
200603 16
< 0.1%

AANTAL_SUBTRAJECT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct10245
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11288.89
Minimum1
Maximum369972
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-06-21T12:44:58.468330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile42
Q1507
median2382
Q39250
95-th percentile53325
Maximum369972
Range369971
Interquartile range (IQR)8743

Descriptive statistics

Standard deviation27113.987
Coefficient of variation (CV)2.4018293
Kurtosis37.016813
Mean11288.89
Median Absolute Deviation (MAD)2209
Skewness5.2661527
Sum3.7392642 × 109
Variance7.351683 × 108
MonotonicityNot monotonic
2023-06-21T12:44:58.638973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 558
 
0.2%
1 533
 
0.2%
4 526
 
0.2%
2 512
 
0.2%
7 471
 
0.1%
17 470
 
0.1%
6 461
 
0.1%
12 455
 
0.1%
25 454
 
0.1%
20 454
 
0.1%
Other values (10235) 326340
98.5%
ValueCountFrequency (%)
1 533
0.2%
2 512
0.2%
3 558
0.2%
4 526
0.2%
5 440
0.1%
6 461
0.1%
7 471
0.1%
8 420
0.1%
9 449
0.1%
10 446
0.1%
ValueCountFrequency (%)
369972 23
< 0.1%
364722 23
< 0.1%
348487 25
< 0.1%
343256 24
< 0.1%
341654 19
< 0.1%
323757 20
< 0.1%
319214 19
< 0.1%
315780 17
< 0.1%
310778 17
< 0.1%
298646 17
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct325
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean677874.62
Minimum190
Maximum1487638
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-06-21T12:44:58.828300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum190
5-th percentile32192
Q1284963
median761460
Q31026359
95-th percentile1340635
Maximum1487638
Range1487448
Interquartile range (IQR)741396

Descriptive statistics

Standard deviation420049.63
Coefficient of variation (CV)0.61965682
Kurtosis-1.134238
Mean677874.62
Median Absolute Deviation (MAD)315834
Skewness-0.076779739
Sum2.2453512 × 1011
Variance1.7644169 × 1011
MonotonicityNot monotonic
2023-06-21T12:44:59.003059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880933 5102
 
1.5%
874099 4354
 
1.3%
843976 4347
 
1.3%
894317 4333
 
1.3%
880476 4273
 
1.3%
897707 4212
 
1.3%
765023 4089
 
1.2%
803443 4029
 
1.2%
737384 3899
 
1.2%
1080888 3890
 
1.2%
Other values (315) 288706
87.2%
ValueCountFrequency (%)
190 65
 
< 0.1%
310 39
 
< 0.1%
325 54
 
< 0.1%
332 125
< 0.1%
349 72
 
< 0.1%
710 185
0.1%
895 19
 
< 0.1%
974 201
0.1%
1583 87
< 0.1%
1610 130
< 0.1%
ValueCountFrequency (%)
1487638 2975
0.9%
1450398 3048
0.9%
1421726 3564
1.1%
1344332 3543
1.1%
1340635 3441
1.0%
1332357 3545
1.1%
1316425 3463
1.0%
1282945 3576
1.1%
1267076 3351
1.0%
1265243 1177
 
0.4%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct325
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1100571.9
Minimum190
Maximum2664767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-06-21T12:44:59.186152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum190
5-th percentile39189
Q1483086
median1106953
Q31804756
95-th percentile2548625
Maximum2664767
Range2664577
Interquartile range (IQR)1321670

Descriptive statistics

Standard deviation750926.87
Coefficient of variation (CV)0.68230604
Kurtosis-0.84182243
Mean1100571.9
Median Absolute Deviation (MAD)649709
Skewness0.29526518
Sum3.6454684 × 1011
Variance5.6389116 × 1011
MonotonicityNot monotonic
2023-06-21T12:44:59.369224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211794 5102
 
1.5%
1281495 4354
 
1.3%
1216252 4347
 
1.3%
1315578 4333
 
1.3%
1300438 4273
 
1.3%
1341839 4212
 
1.3%
1155938 4089
 
1.2%
1205199 4029
 
1.2%
1048692 3899
 
1.2%
2548625 3890
 
1.2%
Other values (315) 288706
87.2%
ValueCountFrequency (%)
190 65
 
< 0.1%
312 39
 
< 0.1%
325 54
 
< 0.1%
333 125
< 0.1%
442 72
 
< 0.1%
761 185
0.1%
913 19
 
< 0.1%
989 201
0.1%
1634 87
< 0.1%
1798 82
< 0.1%
ValueCountFrequency (%)
2664767 3866
1.2%
2662690 3793
1.1%
2619190 3789
1.1%
2594043 3844
1.2%
2548625 3890
1.2%
2480327 3851
1.2%
2282118 3797
1.1%
2178764 3757
1.1%
2062352 3811
1.2%
2052303 1168
 
0.4%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

MISSING 

Distinct3564
Distinct (%)1.3%
Missing53924
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean3596.4582
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-06-21T12:44:59.548630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1480
median1255
Q34190
95-th percentile13700
Maximum287220
Range287150
Interquartile range (IQR)3710

Descriptive statistics

Standard deviation6553.8228
Coefficient of variation (CV)1.8222992
Kurtosis142.45115
Mean3596.4582
Median Absolute Deviation (MAD)1025
Skewness7.1226277
Sum9.9733381 × 108
Variance42952594
MonotonicityNot monotonic
2023-06-21T12:44:59.715895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2086
 
0.6%
105 1951
 
0.6%
110 1791
 
0.5%
185 1672
 
0.5%
180 1602
 
0.5%
300 1415
 
0.4%
175 1393
 
0.4%
145 1379
 
0.4%
120 1357
 
0.4%
140 1325
 
0.4%
Other values (3554) 261339
78.9%
(Missing) 53924
 
16.3%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 919
0.3%
90 668
 
0.2%
95 719
 
0.2%
100 966
0.3%
105 1951
0.6%
110 1791
0.5%
115 1099
0.3%
ValueCountFrequency (%)
287220 8
< 0.1%
148910 3
 
< 0.1%
142835 4
< 0.1%
122155 4
< 0.1%
116765 3
 
< 0.1%
109725 7
< 0.1%
108570 7
< 0.1%
107655 4
< 0.1%
101270 8
< 0.1%
99515 5
< 0.1%

Interactions

2023-06-21T12:44:51.527304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:38.877799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:40.479476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:42.135118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:43.659929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:45.172296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:46.665771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:48.249472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:49.975924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:51.709228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:39.066928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:40.660319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:42.313709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:43.840171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:45.348325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:46.852344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:48.436263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:50.158626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:51.880973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:39.243000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:40.823422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:42.479283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:44.004661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:45.512330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:47.026961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:48.606394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:50.325696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:52.050790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:39.420095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:40.993888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:42.644735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:44.170915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:45.675881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:47.202710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:48.783235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:50.497740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:52.219456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:39.591540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:41.159179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:42.809594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:44.329584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:45.834731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:47.374087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:48.955354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:50.662247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:52.378819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:39.758522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:41.319543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:42.966947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:44.485593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:45.988224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:47.537031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:49.270060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:50.823858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:52.558769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:39.941202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:41.495228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:43.146019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:44.658094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:46.162942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:47.716689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:49.449805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:50.998406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:52.738980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:40.126976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:41.671505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:43.323174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:44.835290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:46.338318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:47.899993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:49.629419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:51.174739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:52.908579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:40.302360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:41.966270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:43.491068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:45.001490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:46.500351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:48.074537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:49.801489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-21T12:44:51.339583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-21T12:44:59.863581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
BEHANDELEND_SPECIALISME_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
BEHANDELEND_SPECIALISME_CD1.0000.2160.0080.013-0.059-0.053-0.542-0.4600.052
ZORGPRODUCT_CD0.2161.000-0.136-0.144-0.168-0.200-0.364-0.3940.028
AANTAL_PAT_PER_ZPD0.008-0.1361.0000.9960.3360.3340.0940.102-0.299
AANTAL_SUBTRAJECT_PER_ZPD0.013-0.1440.9961.0000.3320.3340.0970.109-0.301
AANTAL_PAT_PER_DIAG-0.059-0.1680.3360.3321.0000.9880.3500.3310.030
AANTAL_SUBTRAJECT_PER_DIAG-0.053-0.2000.3340.3340.9881.0000.3630.3600.039
AANTAL_PAT_PER_SPC-0.542-0.3640.0940.0970.3500.3631.0000.960-0.007
AANTAL_SUBTRAJECT_PER_SPC-0.460-0.3940.1020.1090.3310.3600.9601.000-0.009
GEMIDDELDE_VERKOOPPRIJS0.0520.028-0.299-0.3010.0300.039-0.007-0.0091.000

Missing values

2023-06-21T12:44:53.160856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-21T12:44:53.702626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
01.02023-06-072023-06-012018-01-013290999002901011242521981241681345.0
11.02023-06-072023-06-012018-01-0132916990029011546577107711962198124168545.0
21.02023-06-072023-06-012018-01-01329069900290104584673332352621981241681345.0
31.02023-06-072023-06-012018-01-0132908990029012474816216721981241681040.0
41.02023-06-072023-06-012018-01-013291799002901214814869570421981241681040.0
51.02023-06-072023-06-012018-01-0132913990029010303115615721981241681345.0
61.02023-06-072023-06-012018-01-0132999990029011141141370838482198124168545.0
71.02023-06-072023-06-012018-01-0132909990029011161624252198124168545.0
81.02023-06-072023-06-012018-01-0132902990029012172617364872497721981241681040.0
91.02023-06-072023-06-012018-01-0132915990029002149150102910752198124168205.0
VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
3312241.02023-06-072023-06-012016-01-013031431319990941114720513323571831603590.0
3312251.02023-06-072023-06-012015-01-01322190360607008111574619525442154759804430.0
3312261.02023-06-072023-06-012012-01-01303212199299087223747142301148763819395102190.0
3312271.02023-06-072023-06-012014-01-013139441999900611298234561037354206235223300.0
3312281.02023-06-072023-06-012016-01-0130380199035606011153205133235718316034325.0
3312291.02023-06-072023-06-012012-01-013036021992990591130993563148763819395101760.0
3312301.02023-06-072023-06-012014-01-01313916990003004112106239910373542062352105.0
3312311.02023-06-072023-06-012015-01-01301603797990391127042310844281653309355.0
3312321.02023-06-072023-06-012014-01-013137732899903511247147310373542062352710.0
3312331.02023-06-072023-06-012022-01-0130607599035606011221928734958137645384755.0